Zobrazeno 1 - 10
of 15
pro vyhledávání: '"Jianya Yuan"'
Publikováno v:
Journal of Marine Science and Engineering, Vol 12, Iss 3, p 489 (2024)
The complex underwater environment poses significant challenges for unmanned underwater vehicles (UUVs), particularly in terms of communication constraints and the need for precise cooperative obstacle avoidance and trajectory tracking. Addressing th
Externí odkaz:
https://doaj.org/article/f00b08ba97af42c7a83440aada542298
Publikováno v:
Journal of Marine Science and Engineering, Vol 12, Iss 3, p 420 (2024)
UUVs (unmanned underwater vehicles) perform tasks in the marine environment under direction from a commander through a mother ship control system. In cases where communication is available, a UUV task re-planning system was designed to ensure task co
Externí odkaz:
https://doaj.org/article/7fa6483a78654dcd870b3de8b5cf4daa
Publikováno v:
Journal of Marine Science and Engineering, Vol 11, Iss 12, p 2258 (2023)
Collision avoidance planning has always been a hot and important issue in the field of unmanned aircraft research. In this article, we describe an online collision avoidance planning algorithm for autonomous underwater vehicle (AUV) autonomous naviga
Externí odkaz:
https://doaj.org/article/2998580de9aa47dfb460b08c8314ff99
Publikováno v:
IEEE Access, Vol 7, Pp 15140-15151 (2019)
A dynamic path planning method based on a gated recurrent unit-recurrent neural network model is proposed for the problem of path planning of a mobile robot in an unknown space. A deep neural network with sensor input is used to generate a new contro
Externí odkaz:
https://doaj.org/article/c3861ab3c8ed465a9088c844a5434413
Publikováno v:
Journal of Marine Science and Engineering, Vol 9, Iss 11, p 1166 (2021)
In a complex underwater environment, finding a viable, collision-free path for an autonomous underwater vehicle (AUV) is a challenging task. The purpose of this paper is to establish a safe, real-time, and robust method of collision avoidance that im
Externí odkaz:
https://doaj.org/article/aa4f8fea66634cbb8579ebfc57708835
Publikováno v:
Complexity, Vol 2019 (2019)
In this paper, we present an online obstacle avoidance planning method for unmanned underwater vehicle (UUV) based on clockwork recurrent neural network (CW-RNN) and long short-term memory (LSTM), respectively. In essence, UUV online obstacle avoidan
Externí odkaz:
https://doaj.org/article/4a87c413463f4818991a949515ff0a51
Publikováno v:
IEEE Transactions on Intelligent Vehicles. 8:2319-2331
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1b4c96f6daeb404c2c29cac597cef05f
https://doi.org/10.1109/tiv.2023.3245615
https://doi.org/10.1109/tiv.2023.3245615
Publikováno v:
IEEE Transactions on Instrumentation and Measurement. 70:1-12
Target state estimation is a key technology for unmanned underwater vehicles (UUVs) to achieve target tracking, collision avoiding, formation control, and other tasks. Compared with other measurement methods, underwater measurement has lower reliabil
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::15f1a32daf3a43176aff301e1f0703b8
https://doi.org/10.1109/tim.2020.3011789
https://doi.org/10.1109/tim.2020.3011789
Publikováno v:
Complexity, Vol 2019 (2019)
In this paper, we present an online obstacle avoidance planning method for unmanned underwater vehicle (UUV) based on clockwork recurrent neural network (CW-RNN) and long short-term memory (LSTM), respectively. In essence, UUV online obstacle avoidan
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::79616b5e8b825878f3639db12ce21f68
https://doi.org/10.1155/2019/6320186
https://doi.org/10.1155/2019/6320186
Publikováno v:
IEEE Access, Vol 7, Pp 15140-15151 (2019)
A dynamic path planning method based on a gated recurrent unit-recurrent neural network model is proposed for the problem of path planning of a mobile robot in an unknown space. A deep neural network with sensor input is used to generate a new contro
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::10f1c8f52ffcd58231fde3ebdc478e76
https://ieeexplore.ieee.org/document/8625490/
https://ieeexplore.ieee.org/document/8625490/